Peer-led safer supply and opioid agonist treatment medication distribution: a case study from rural British Columbia
Bibliographic record
Abstract
BACKGROUND: British Columbia (BC) has been facing a public health emergency of overdose since 2016, with rural regions of the province facing the highest rates of death. Peers (in this case, people with lived experience of substance use) are known to be effective patient navigators in health systems and can play a role in connecting patients to care and reducing overdose risk. CASE PRESENTATION: We outline a peer-led program focused on opioid agonist treatment and prescribed safe supply medication delivery that began in March 2020 at a clinic in rural BC. The peer takes an Indigenous harm reduction approach and is focused on meeting the needs of the whole person. The peer has regular contact with approximately 50 clients and navigates medication delivery and appointments for approximately 10-15 people each day. Clients have been retained on the medication, and experienced improvement in other outcomes, including securing housing, employment and managing acute and chronic health conditions. The peer has established contact with clients since March 2020 to support engagement with health care and continuity of medication access. This program highlights the importance and value of peer-led work and need for further investments in peer-led programs to respond to the unregulated drug poisoning crisis. CONCLUSIONS: This peer-led intervention is a promising approach to engaging people who remain disconnected from health services in care in a rural community. This model could be adapted to other settings to support patient contact with the health system and medication access and continuity, with the ultimate goal of reducing overdose risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".